Regression Statistics Multiple R 0.9086 R square A Adjusted R square 0.8181 standard Error 398.0910 Observations B anova df SS MS F Significance F Regression 2 D E G 1.50876E-18 Residual C 7448393.148 F Total 49 42699148.82 coefficients Standard Error t-stats p-value intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($ 1000s) 33.1330 3.9679 H 7.68206E-11 Household size 356.2959 33.2009 10.7315 3.12342E-14 The multiple regression output is based on data collected by a research company on annual income, household size and annual credit charges for a sample if 50 consumers. Compute the missing entries from A to H in this output.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
| Regression | Statistics |
| Multiple R | 0.9086 |
| R square | A |
| Adjusted R square | 0.8181 |
| standard Error | 398.0910 |
| Observations | B |
| anova | |||||
| df | SS | MS | F | Significance F | |
| Regression | 2 | D | E | G | 1.50876E-18 |
| Residual | C | 7448393.148 | F | ||
| Total | 49 | 42699148.82 |
| coefficients | Standard Error | t-stats | p-value | |
| intercept | 1304.9048 | 197.6548 | 6.6019 | 3.28664E-08 |
| Income ($ 1000s) | 33.1330 | 3.9679 | H | 7.68206E-11 |
| Household size | 356.2959 | 33.2009 | 10.7315 | 3.12342E-14 |
The multiple regression output is based on data collected by a research company on annual income, household size and annual credit charges for a sample if 50 consumers. Compute the missing entries from A to H in this output.
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